Fine-needle aspiration cytology (FNAC) is the cornerstone of thyroid nodule evaluation, standardized by the Bethesda System. However, indeterminate categories (Bethesda III–IV) remain a major challenge, often leading to unnecessary surgery or delayed molecular testing. Deep learning (DL) has recently emerged as a promising adjunct in thyroid cytopathology, with applications spanning triage support, Bethesda category classification, and integration with molecular data. Yet, routine adoption is limited by preanalytical variability (staining, slide preparation, Z-stack acquisition, scanner heterogeneity), annotation bias, and domain shift, which reduce generalizability across centers. Most studies remain retrospective and single-institution, with limited external validation. This article provides a technical overview of DL in thyroid cytology, emphasizing preanalytical sources of variability, architectural choices, and potential clinical applications. We argue that standardized datasets, multicenter prospective trials, and robust explainability frameworks are essential prerequisites for safe clinical deployment. Looking forward, DL systems are most likely to enter practice as diagnostic co-pilots, Bethesda classifiers, and multimodal risk-stratification tools. With rigorous validation and ethical oversight, these technologies may augment cytopathologists, reduce interobserver variability, and help transform thyroid cytology into a more standardized and data-driven discipline.
细针穿刺细胞学（FNAC）是甲状腺结节评估的基石，其诊断标准已通过Bethesda系统实现标准化。然而，不确定类别（Bethesda III–IV级）仍是主要挑战，常导致不必要的手术或延迟分子检测。深度学习（DL）近年来已成为甲状腺细胞病理学中极具前景的辅助工具，其应用涵盖分诊支持、Bethesda分级分类及与分子数据整合等多个领域。但分析前变异（染色、玻片制备、Z轴堆叠采集、扫描仪异质性）、标注偏倚及领域偏移等问题限制了其常规应用，降低了跨中心泛化能力。目前多数研究仍为回顾性单中心设计，外部验证有限。本文系统综述了深度学习在甲状腺细胞学中的技术要点，重点探讨分析前变异来源、架构设计选择及潜在临床应用场景。我们认为，标准化数据集、多中心前瞻性试验及稳健的可解释性框架是实现安全临床部署的必要前提。展望未来，深度学习系统最可能以诊断协作者、Bethesda分级器及多模态风险分层工具的形式进入临床实践。通过严格验证与伦理监管，这些技术有望增强细胞病理学家诊断能力，减少观察者间差异，推动甲状腺细胞学向更标准化、数据驱动的学科方向发展。
Artificial Intelligence in Thyroid Cytopathology: Diagnostic and Technical Insights
肿瘤(癌症)患者之家